Respiratory Blower Acoustic Monitoring for Deterioration Detection
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Solution Overview
Problem
Existing respiratory therapy devices suffer from acoustic noise due to blower deterioration, which affects user comfort and requires costly servicing, and current monitoring methods are inaccurate and difficult to implement.
Innovation Solution
Acoustic monitoring of respiratory therapy devices using Fourier analysis of sound signals to detect blower deterioration by analyzing patterns of peaks and correlations in the frequency domain, enabling timely detection and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If acoustic monitoring is implemented to detect blower deterioration, then device reliability is improved, but device complexity increases due to additional sensors and signal processing systems
Solution Approach 1:
The system uses the blower's own acoustic emissions to monitor its health status. The microphone captures sounds generated by the blower during normal operation, and the processing system analyzes these self-generated signals to detect deterioration, eliminating the need for separate active testing mechanisms.
Solution Approach 2:
The patent replaces complex mechanical vibration analysis with acoustic signal processing. Instead of using vibration sensors and mechanical analysis systems, the invention uses microphones to capture acoustic signals and processes these signals through Fourier transforms and pattern recognition algorithms to detect blower deterioration.
2Reliability
If continuous acoustic monitoring is performed, then device reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs acoustic monitoring periodically rather than continuously. The processing system analyzes acoustic signals at predetermined intervals during blower operation, capturing snapshots of the acoustic spectrum at key moments rather than requiring continuous processing, thereby reducing energy consumption while maintaining monitoring effectiveness.
3Measurement precision
If sophisticated signal processing algorithms are used to improve detection accuracy, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The signal processing is divided into distinct sequential stages: acoustic signal capture, Fourier transform conversion, peak identification, pattern matching against reference spectra, and deterioration determination. Each stage processes a specific aspect of the signal independently, making the complex overall process more manageable and implementable.
Solution Approach 2:
The system pre-stores reference acoustic spectra representing healthy blower operation at different speeds and conditions. These reference patterns are prepared in advance, allowing the real-time monitoring system to simply compare current acoustic signals against the pre-stored references using pattern matching algorithms, rather than requiring complex real-time analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately monitors blower status, improving user comfort and reducing servicing costs by identifying device deterioration early, ensuring safe and efficient operation.
Implementation Method 1
a sound sensor located in an air path of the respiratory therapy device and configured to sense a sound wave in the air path generated by the blower
Implementation Method 2
The processor is configured to compute a Fourier analysis of the sound signal generated by the sound sensor
Data Source
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AI summary
Systems and methods implement a status assessment of a respiratory device with sound evaluation. In some versions, the system may include a motor operated pressure generator to generate airflow through an air circuit to a patient interface. The system may include a transducer producing a signal representing sound of the generator in the circuit. The system may include a controller, such as with a processor, for generator control. The system, such as with the controller, computes a sound representation in the frequency domain. The system applies, to the frequency domain representation, any one or more of an integer-multiple function, non-integer-multiple function, a statistical correlation function and resonant frequency function, such as of a fundamental frequency attributable to motor operation. The system may derive a noise vector with data from the function(s). The system may classify the vector to obtain a status indicator of the generator and generate indicator related output.